Row 3877

Row ID: 3877 | Dataset Entry | Axioma AXP Content Repository

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Hello guys I am new to quantum computing and im trying to build a QVC for heart dieseas and im using this dataset [https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset)

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Are any of you familiar with building variational quantum circuits (VQCs) for datasets like the one containing information about heart disease? I'm unsure about the best approach to handle the high dimensionality. Should I reduce the number of features using techniques like principal component analysis (PCA), or is it unnecessary? Has anyone here had experience building VQCs for similar datasets, and if so, what strategies did you find most effective? Any insights or advice would be greatly appreciated!

Also would you say that using QSVM or QNN would be easier to do with pennylane ?

FieldValue
text Hello guys I am new to quantum computing and im trying to build a QVC for heart dieseas and im using this dataset [https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset) ​ Are any of you familiar with building variational quantum circuits (VQCs) for datasets like the one containing information about heart disease? I'm unsure about the best approach to handle the high dimensionality. Should I reduce the number…
label r/quantumcomputing
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communityName r/QuantumComputing
datetime 2024-03-28
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{
  "text": "Hello guys I am new to quantum computing and im trying to build a QVC for heart dieseas and im using this dataset [https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset)\n\n​\n\nAre any of you familiar with building variational quantum circuits (VQCs) for datasets like the one containing information about heart disease? I'm unsure about the best approach to handle the high dimensionality. Should I reduce the number of features using techniques like principal component analysis (PCA), or is it unnecessary? Has anyone here had experience building VQCs for similar datasets, and if so, what strategies did you find most effective? Any insights or advice would be greatly appreciated!\n\nAlso would you say that using QSVM or QNN would be easier to do with pennylane ?",
  "label": "r/quantumcomputing",
  "dataType": "post",
  "communityName": "r/QuantumComputing",
  "datetime": "2024-03-28",
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}
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